Grouping materials and processes for the designer: an application for cluster analysis.

In this paper, an analytical tool - cluster analysis - that is commonly used in biology, archaeology, linguistics and psychology is applied to materials and design. Here we use it to cluster materials and the processes that shape them, using their attributes as indicators of relationship. The attributes that are chosen are important to design and designers. The resulting clusters, and the classifications that can be developed from them, depend on the selected attributes and - to some extent - on the method of clustering. Alternative classifications for design that is focused on the technical or aesthetic attributes of materials and the materials and shapes allowed by processes are explored.

Cluster analysis of heterogeneous rank data

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.